基于SURF特征和Mean-shift算法的智能车辆跟踪技术

Liu Yang, Wang Zhong-li, Cai Bai-gen
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引用次数: 6

摘要

在交通视频监控系统中,目标级跟踪和特征级跟踪是两个重要的研究领域。因此,它们之间的结合是一个有趣的问题。Mean-shift是一种传统的目标级跟踪算法,不适应车辆尺度和方向的变化。为了解决这一问题,本文提出了将SURF(加速鲁棒特征)特征与Mean-shift算法相结合的算法。利用特征点尺度和方向信息,使算法具有尺度和方向的自适应性。该算法还对车辆的跟踪模型进行了更新。实验结果表明,该算法比传统的车辆尺度和方向变化跟踪算法具有更好的跟踪效果。此外,跟踪结果也更加准确。
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An intelligent vehicle tracking technology based on SURF feature and Mean-shift algorithm
In traffic video surveillance system, target-level tracking and feature-level tracking are two important areas for research. Therefore, the combination between them is an interesting question. Mean-shift is a traditional target-level tracking algorithm with no adaptation to vehicle scale and orientation change. In order to solve the problem, algorithm combine SURF (speed-up robust feature) feature with Mean-shift algorithm is proposed in this article. Feature point scale and orientation information is used to make algorithm with scale and orientation adaptability. The tracking model of the vehicle is also updated in the algorithm. Experimental results show that the proposed algorithm provides better tracking result than traditional algorithm of vehicle scale and orientation change. Furthermore, the tracking result is also more accurate.
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